Jeremy Howard — The Simple but Profound Insight Behind Diffusion

Jeremy Howard is a co-founder of fast.ai, the non-profit research group behind the popular massive open online course "Practical Deep Learning for Coders", and the open source deep learning library "fastai".Jeremy is also a co-founder of #Masks4All, a global volunteer organization founded in March 2020 that advocated for the public adoption of homemade face masks in order to help slow the spread of COVID-19. His Washington Post article "Simple DIY masks could help flatten the curve." went viral in late March/early April 2020, and is associated with the U.S CDC's change in guidance a few days later to recommend wearing masks in public.In this episode, Jeremy explains how diffusion works and how individuals with limited compute budgets can engage meaningfully with large, state-of-the-art models. Then, as our first-ever repeat guest on Gradient Dissent, Jeremy revisits a previous conversation with Lukas on Python vs. Julia for machine learning.Finally, Jeremy shares his perspective on the early days of COVID-19, and what his experience as one of the earliest and most high-profile advocates for widespread mask-wearing was like.Show notes (transcript and links): http://wandb.me/gd-jeremy-howard-2---⏳ Timestamps: 0:00 Intro1:06 Diffusion and generative models14:40 Engaging with large models meaningfully20:30 Jeremy's thoughts on Stable Diffusion and OpenAI26:38 Prompt engineering and large language models32:00 Revisiting Julia vs. Python40:22 Jeremy's science advocacy during early COVID days1:01:03 Researching how to improve children's education1:07:43 The importance of executive buy-in1:11:34 Outro1:12:02 Bonus: Weights & Biases---📝 Links📍 Jeremy's previous Gradient Dissent episode (8/25/2022): http://wandb.me/gd-jeremy-howard📍 "Simple DIY masks could help flatten the curve. We should all wear them in public.", Jeremy's viral Washington Post article: https://www.washingtonpost.com/outlook/2020/03/28/masks-all-coronavirus/📍 "An evidence review of face masks against COVID-19" (Howard et al., 2021), one of the first peer-reviewed papers on the effectiveness of wearing masks: https://www.pnas.org/doi/10.1073/pnas.2014564118📍 Jeremy's Twitter thread summary of "An evidence review of face masks against COVID-19": https://twitter.com/jeremyphoward/status/1348771993949151232📍 Read more about Jeremy's mask-wearing advocacy: https://www.smh.com.au/world/north-america/australian-expat-s-push-for-universal-mask-wearing-catches-fire-in-the-us-20200401-p54fu2.html---Connect with Jeremy and fast.ai:📍 Jeremy on Twitter: https://twitter.com/jeremyphoward📍 fast.ai on Twitter: https://twitter.com/FastDotAI📍 Jeremy on LinkedIn: https://www.linkedin.com/in/howardjeremy/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan

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Gradient Dissent is a machine learning podcast from Weights & Biases with hosts Lukas Biewald, Lavanya Shukla and Caryn Marooney. It takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at NVIDIA, Meta, Google, Lyft, OpenAI, and more.